Abstract:
Affective computing aims to implement methods and technologies to recognize and synthesize human emotions. Understanding human facial expressions is essential to the success of
this new branch of AI.
Emotions can be conveyed through various channels, the most prominent are facial expressions, speech, texts and various other physiological signals. This topic has occupied researchers
for a long time due to the difficulty of understanding and categorizing these expressions.
In this work, we explore the different techniques carried out to recognize facial emotions in
videos. We experiment on the AFEW dataset with two models based on deep learning. The
first uses TCNs and the second uses CNNs.
The experience with the first model was very hard since it belongs to recent sequential
models and was not completed due to difficulty of implementation and limited resources. The
second model achieved good accuracy of up to 91%.
